Radius-SMOTE: A New Oversampling Technique of Minority Samples Based on Radius Distance for Learning From Imbalanced Data

Radius-SMOTE: A New Oversampling Technique of Minority Samples Based on Radius Distance for Learning From Imbalanced Data

Imbalanced learning problems are a challenge faced by classifiers when data samples have an unbalanced distribution in each class. Furthermore, the synthetic oversampling method (SMOTE) is a preprocessing technique widely used to synthesize new data and balance the different numbers of samples in ea...

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Bibliographic Details
Main Authors: Gede Angga Pradipta, Retantyo Wardoyo, Aina Musdholifah, I Nyoman Hariyasa Sanjaya
Format: Article
Language:English
Published: IEEE 2021-01-01
Series:IEEE Access
Subjects:
Imbalanced learning
oversampling
SMOTE
radius distance
initial selection
Online Access:https://ieeexplore.ieee.org/document/9431216/

Internet

https://ieeexplore.ieee.org/document/9431216/

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